FalkorDBEmbeddingRetriever

An embedding-based Retriever compatible with the FalkorDB Document Store.

Most common position in a pipeline	1. After a Text Embedder and before a PromptBuilder in a RAG pipeline 2. The last component in a semantic search pipeline
Mandatory init variables	document_store: An instance of a FalkorDBDocumentStore
Mandatory run variables	query_embedding: A vector representing the query (a list of floats)
Output variables	documents: A list of documents
API reference	FalkorDB
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/falkordb
Package name	falkordb-haystack

Overview

The FalkorDBEmbeddingRetriever retrieves documents from a FalkorDBDocumentStore using FalkorDB's native vector index. It compares the query embedding with document embeddings and returns the most similar documents.

In addition to query_embedding, the retriever accepts optional filters to narrow the search space and top_k to limit the number of results.

The embedding dimension and similarity function are configured on the FalkorDBDocumentStore at initialization time.

Installation

shell

pip install falkordb-haystack

Ensure FalkorDB is running, for example via Docker:

shell

docker run -d -p 6379:6379 falkordb/falkordb:latest

Usage

On its own

python

from haystack import Document
from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
from haystack_integrations.components.retrievers.falkordb import (
    FalkorDBEmbeddingRetriever,
)

document_store = FalkorDBDocumentStore(
    host="localhost",
    port=6379,
    embedding_dim=3,
    recreate_graph=True,
)
document_store.write_documents(
    [
        Document(
            content="There are over 7,000 languages spoken around the world today.",
            embedding=[0.1, 0.2, 0.3],
        ),
        Document(
            content="Elephants have been observed to recognize themselves in mirrors.",
            embedding=[0.8, 0.1, 0.5],
        ),
    ],
)

retriever = FalkorDBEmbeddingRetriever(document_store=document_store, top_k=1)
result = retriever.run(query_embedding=[0.1, 0.2, 0.3])
print(result["documents"][0].content)

In a pipeline

python

from haystack import Document, Pipeline
from haystack.document_stores.types import DuplicatePolicy
from haystack.components.embedders import (
    SentenceTransformersDocumentEmbedder,
    SentenceTransformersTextEmbedder,
)
from haystack_integrations.document_stores.falkordb import FalkorDBDocumentStore
from haystack_integrations.components.retrievers.falkordb import (
    FalkorDBEmbeddingRetriever,
)

document_store = FalkorDBDocumentStore(
    host="localhost",
    port=6379,
    embedding_dim=384,
    recreate_graph=True,
)

documents = [
    Document(content="There are over 7,000 languages spoken around the world today."),
    Document(
        content="Elephants have been observed to recognize themselves in mirrors.",
    ),
    Document(
        content="Bioluminescent waves can be seen in the Maldives and Puerto Rico.",
    ),
]

document_embedder = SentenceTransformersDocumentEmbedder(
    model="sentence-transformers/all-MiniLM-L6-v2",
)
document_embedder.warm_up()
documents_with_embeddings = document_embedder.run(documents)

document_store.write_documents(
    documents_with_embeddings["documents"],
    policy=DuplicatePolicy.OVERWRITE,
)

query_pipeline = Pipeline()
query_pipeline.add_component(
    "text_embedder",
    SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"),
)
query_pipeline.add_component(
    "retriever",
    FalkorDBEmbeddingRetriever(document_store=document_store, top_k=3),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

result = query_pipeline.run(
    {"text_embedder": {"text": "How many languages are there?"}},
)
print(result["retriever"]["documents"][0].content)